Technical Field
Various embodiments relate to the processing of signals originating from at least one sensor, in particular but not exclusively proximity sensors, for the recognition of a movement of an object, for example a hand movement made above a tablet, a cellular mobile telephone or other similar electronic device, and with which an action is associated.
Description of the Related Art
Proximity sensors are sensors capable of detecting the presence of a nearby object without any physical contact. These sensors are known to those skilled in the art. The proximity sensors marketed by STMicroelectronics under the reference VL6180X may for example be used.
These sensors emit an infrared beam in the direction of an object and measure the time-of-flight (TOF technology) of this beam, in other words the time taken between its emission and its reception by the sensor after reflection on the object.
They can supply several pieces of information, notably information relating to the distance between the sensor and the object, the amplitude of the signal received by the sensor, the coverage of the object, in other words the percentage of the surface of the emission cone covered by the object, or else the convergence time of the sensor, in other words the time needed by the sensor for determining the time-of-flight of the emitted beam. This convergence time depends, in part, on the distance that separates the object from the sensor.
When no object is detected, these data have a value equal to a reference value. The latter differs depending on the type of information and corresponds, for example for the distance information, to the maximum detection distance of the sensor.
The problem of recognition of a movement of an object, and notably of a hand movement, may be seen as a problem of classification or of recognition of shapes.
There exist numerous algorithms for classification and for recognition of shapes amongst which may be mentioned:
support vector machines (or SVMs), described in the article “A Tutorial on Support Vector Machines for Pattern Recognition”, C. J. C. Burges, in Data Mining and Knowledge Discovery 2, pp. 121-167, 1998;
naïve Bayes classification, described for example in “An empirical study of the naïve Bayes classifier”, I. Rish, IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, 2001; and
dynamic time warping (or DTW), described in “Using Dynamic Time Warping to Find Patterns in Time Series”, D. J. Benrdt, AAA1-94 Workshop on Knowledge Discovery in Databases, pp. 359-370, 1994.
Support vector machines and Bayes classification require vectors of a fixed size (whose number of components does not vary) at the input, whereas dynamic time warping can operate with vectors of variable size.